Meta Data Services

About: Meta Data Services is a research topic. Over the lifetime, 2564 publications have been published within this topic receiving 40102 citations.

An Emergent Theory of Digital Library Metadata: Enrich then Filter

Abstract: An Emergent Theory of Digital Library Metadata is a reaction to the current digital library landscape that is being challenged with growing online collections and changing user expectations. The theory provides the conceptual underpinnings for a new approach which moves away from expert defined standardised metadata to a user driven approach with users as metadata co-creators. Moving away from definitive, authoritative, metadata to a system that reflects the diversity of users' terminologies, it changes the current focus on metadata simplicity and efficiency to one of metadata enriching, which is a continuous and evolving process of data linking. From predefined description to information conceptualised, contextualised and filtered at the point of delivery. By presenting this shift, this book provides a coherent structure in which future technological developments can be considered. * Metadata is valuable when continuously enriched by experts and users* Metadata enriching results from ubiquitous linking * Metadata is a resource that should be linked openly* The power of metadata is unlocked when enriched metadata is filtered for users individually

WPAR: A Weight-Based Metadata Management Strategy for Petabyte-Scale Object Storage Systems

Abstract: In an object-based network storage system, metadata access is decoupled from the data transferring path to improve system performance, scalability, and management. Designing an efficient metadata management scheme in such a system is critically important to the overall system performance and poses lots of challenges to the system designers. Traditional metadata management schemes, either partitioning the metadata statically, or using pure hashing methods, are not able to adapt to the dynamic metadata workload, and hence suffer from scalability and hotspot problems. In this paper, we propose a new metadata management strategy, referred to as Weighted Partitioning and Adaptive Replication (WPAR) scheme, taking into account the weight (CPUpower, memory capacity, network bandwidth, etc.) of each metadata server. Our objective is to efficiently distribute and dynamically replicate the metadata, based on the weights, to balance the workload and reduce hotspots within the metadata server cluster.

Methods and systems for enhancing metadata

Abstract: A method and system for utilizing metadata to search for media, such as multimedia and streaming media, includes searching for the media, receiving results, extracting metadata associated with the media, enhancing the extracted metadata, and grouping the search results in accordance with attributes of the enhanced metadata. Enhancing and grouping include adding related metadata to the database of metadata, iteratively using metadata to search for more media related data, removing duplicate URLs, collapsing URLs that are variants of each other, and masking out superfluous terms from URLs. The resultant metadata and media files are available to users and search engines.

Method and apparatus for providing metadata of content, and method and apparatus for limiting content usage authority

Abstract: Provided are a method and apparatus for limiting a content usage authority scope of a client by using metadata in an Internet Protocol Television (IPTV) service. The method performed by a metadata server adds control information to the metadata and transmits the metadata, wherein the content usage authority scope of the client is defined in the control information. The client of a user limits a content control operation according to a user input by using the control information extracted from the metadata.

What's up LOD Cloud?

Abstract: Linked Open Data LOD has emerged as one of the largest collections of interlinked datasets on the web. In order to benefit from this mine of data, one needs to access descriptive information about each dataset or metadata. However, the heterogeneous nature of data sources reflects directly on the data quality as these sources often contain inconsistent as well as misinterpreted and incomplete metadata information. Considering the significant variation in size, the languages used and the freshness of the data, one realizes that finding useful datasets without prior knowledge is increasingly complicated. We have developed Roomba, a tool that enables to validate, correct and generate dataset metadata. In this paper, we present the results of running this tool on parts of the LOD cloud accessible via the datahub.io API. The results demonstrate that the general state of the datasets needs more attention as most of them suffers from bad quality metadata and lacking some informative metrics that are needed to facilitate dataset search. We also show that the automatic corrections done by Roomba increase the overall quality of the datasets metadata and we highlight the need for manual efforts to correct some important missing information.